Time-Series Analysis of Ball Carrier Open-Space (BCOS) in Association Football

Abstract

Assessing team performance in association football (commonly known as football or soccer) is challenging due to the sport’s low-scoring nature and inherent unpredictability. While evaluating strategies based on space control and the creation of open spaces has been explored in the literature, the temporal aspect of space availability for the ball carrier remains under-explored. This work introduces a novel time-series performance evaluation metric, Ball Carrier Open Space (BCOS), which focuses on the temporal dynamics of space available to the ball carrier to assess team performance. Additionally, it presents a novel approach to quantify open space for the ball carrier using player data extracted from television footage. This work discuss on BCOS in defensive third, central third and attacking third and a machine learning model is developed to evaluate their significance and temporal patterns. Trained model achieved 80.7% accuracy in classifying match-winning performances, underscoring the significance of BCOS. Correlation analysis between temporal features and match outcomes further reveals that BCOS in central third and attacking third are more important for match winning outcomes, while first-half performance plays a more critical role in determining match results than second-half performance. Based on the results of the correlation analysis, this study proposes a weighted ball carrier open space (wBCOS) metric to assess team performance, assigning weights to BCOS in attacking third, central third and defensive third based on their contributions to positive match outcomes. A machine learning model trained using wBCOS achieved an 82.5% accuracy in classifying match-winning performances, surpassing the performance of any previously published match-winner classification model.

Publication DOI: https://doi.org/10.1007/s42979-025-03815-7
Divisions: College of Engineering & Physical Sciences > Aston Digital Futures Institute
College of Engineering & Physical Sciences
Aston University (General)
Funding Information: Open Access funding enabled and organized by CAUL and its Member Institutions. This research was funded by the Deakin-Coventry Cotutelle Scholarship program. The authors gratefully acknowledge the financial assistance provided by this scholarship, which e
Additional Information: Copyright © The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords: Performance evaluation,Machine learning,Time-series,Open space,Football,Soccer
Publication ISSN: 2661-8907
Data Access Statement: The dataset used for this study is publicly available and can be accessed from statsbomb open-data Github repository at https:// github. com/ stats bomb/ open- data.
Last Modified: 21 Mar 2025 08:13
Date Deposited: 20 Mar 2025 09:31
Full Text Link:
Related URLs: https://link.sp ... 979-025-03815-7 (Publisher URL)
PURE Output Type: Article
Published Date: 2025-03-19
Published Online Date: 2025-03-19
Accepted Date: 2025-02-20
Authors: Bandara, Ishara
Shelyag, Sergiy
Rajasegarar, Sutharshan
Dwyer, Daniel B.
Kim, Eun-jin
Angelova, Maia (ORCID Profile 0000-0002-0931-0916)

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License: Creative Commons Attribution


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